{ "cells": [ { "cell_type": "markdown", "source": [ "# Basic information about a data frame" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "using DataFrames" ], "metadata": {}, "execution_count": 1 }, { "cell_type": "markdown", "source": [ "Let's start by creating a `DataFrame` object, `x`, so that we can learn how to get information on that data frame." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\u001b[1m C \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m String \u001b[0m\n─────┼──────────────────────────\n 1 │ 1 1.0 a\n 2 │ 2 \u001b[90m missing \u001b[0m b", "text/html": [ "
2×3 DataFrame
RowABC
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" ] }, "metadata": {}, "execution_count": 2 } ], "cell_type": "code", "source": [ "x = DataFrame(A=[1, 2], B=[1.0, missing], C=[\"a\", \"b\"])" ], "metadata": {}, "execution_count": 2 }, { "cell_type": "markdown", "source": [ "The standard `size` function works to get dimensions of the `DataFrame`," ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "((2, 3), 2, 3)" }, "metadata": {}, "execution_count": 3 } ], "cell_type": "code", "source": [ "size(x), size(x, 1), size(x, 2)" ], "metadata": {}, "execution_count": 3 }, { "cell_type": "markdown", "source": [ "as well as `nrow` and `ncol` from R." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "(2, 3)" }, "metadata": {}, "execution_count": 4 } ], "cell_type": "code", "source": [ "nrow(x), ncol(x)" ], "metadata": {}, "execution_count": 4 }, { "cell_type": "markdown", "source": [ "`describe` gives basic summary statistics of data in your `DataFrame` (check out the help of `describe` for information on how to customize shown statistics)." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m3×7 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m variable \u001b[0m\u001b[1m mean \u001b[0m\u001b[1m min \u001b[0m\u001b[1m median \u001b[0m\u001b[1m max \u001b[0m\u001b[1m nmissing \u001b[0m\u001b[1m eltype \u001b[0m\n │\u001b[90m Symbol \u001b[0m\u001b[90m Union… \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Union… \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Type \u001b[0m\n─────┼───────────────────────────────────────────────────────────────────────\n 1 │ A 1.5 1 1.5 2 0 Int64\n 2 │ B 1.0 1.0 1.0 1.0 1 Union{Missing, Float64}\n 3 │ C \u001b[90m \u001b[0m a \u001b[90m \u001b[0m b 0 String", "text/html": [ "
3×7 DataFrame
Rowvariablemeanminmedianmaxnmissingeltype
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" ] }, "metadata": {}, "execution_count": 5 } ], "cell_type": "code", "source": [ "describe(x)" ], "metadata": {}, "execution_count": 5 }, { "cell_type": "markdown", "source": [ "you can limit the columns shown by `describe` using `cols` keyword argument" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×7 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m variable \u001b[0m\u001b[1m mean \u001b[0m\u001b[1m min \u001b[0m\u001b[1m median \u001b[0m\u001b[1m max \u001b[0m\u001b[1m nmissing \u001b[0m\u001b[1m eltype \u001b[0m ⋯\n │\u001b[90m Symbol \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Real \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Real \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Type \u001b[0m ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ A 1.5 1 1.5 2 0 Int64 ⋯\n 2 │ B 1.0 1.0 1.0 1.0 1 Union{Missing, Float6\n\u001b[36m 1 column omitted\u001b[0m", "text/html": [ "
2×7 DataFrame
Rowvariablemeanminmedianmaxnmissingeltype
SymbolFloat64RealFloat64RealInt64Type
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" ] }, "metadata": {}, "execution_count": 6 } ], "cell_type": "code", "source": [ "describe(x, cols=1:2)" ], "metadata": {}, "execution_count": 6 }, { "cell_type": "markdown", "source": [ "`names` will return the names of all columns as strings" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "3-element Vector{String}:\n \"A\"\n \"B\"\n \"C\"" }, "metadata": {}, "execution_count": 7 } ], "cell_type": "code", "source": [ "names(x)" ], "metadata": {}, "execution_count": 7 }, { "cell_type": "markdown", "source": [ "you can also get column names with a given element type (`eltype`):" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "1-element Vector{String}:\n \"C\"" }, "metadata": {}, "execution_count": 8 } ], "cell_type": "code", "source": [ "names(x, String)" ], "metadata": {}, "execution_count": 8 }, { "cell_type": "markdown", "source": [ "use `propertynames` to get a vector of `Symbol`s:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "3-element Vector{Symbol}:\n :A\n :B\n :C" }, "metadata": {}, "execution_count": 9 } ], "cell_type": "code", "source": [ "propertynames(x)" ], "metadata": {}, "execution_count": 9 }, { "cell_type": "markdown", "source": [ "`eltype` on `eachcol(x)` returns element types of columns:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "3-element Vector{Type}:\n Int64\n Union{Missing, Float64}\n String" }, "metadata": {}, "execution_count": 10 } ], "cell_type": "code", "source": [ "eltype.(eachcol(x))" ], "metadata": {}, "execution_count": 10 }, { "cell_type": "markdown", "source": [ "Here we create some large `DataFrame`" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m1000×10 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\u001b[1m x4 \u001b[0m\u001b[1m x5 \u001b[0m\u001b[1m x6 \u001b[0m\u001b[1m x7 \u001b[0m\u001b[1m x8 \u001b[0m\u001b[1m x9 \u001b[0m\u001b[1m x10 \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n──────┼──────────────────────────────────────────────────────────────────────\n 1 │ 1 3 6 5 3 3 1 8 8 3\n 2 │ 10 8 1 7 8 3 10 10 7 9\n 3 │ 9 9 9 4 2 8 3 4 10 8\n 4 │ 6 4 6 7 8 2 3 5 8 1\n 5 │ 3 1 8 5 4 6 4 8 5 1\n 6 │ 7 2 1 7 3 8 6 8 2 4\n 7 │ 2 9 9 4 6 7 8 7 2 1\n 8 │ 3 3 5 10 7 7 8 2 2 7\n ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮\n 994 │ 2 9 2 9 3 2 8 3 1 1\n 995 │ 2 8 4 3 10 10 3 7 5 2\n 996 │ 7 3 1 7 5 5 2 2 3 2\n 997 │ 7 4 7 2 2 6 8 4 6 8\n 998 │ 4 8 1 1 3 3 2 3 8 8\n 999 │ 1 9 4 4 10 6 4 3 8 2\n 1000 │ 7 1 4 2 1 8 1 1 7 3\n\u001b[36m 985 rows omitted\u001b[0m", "text/html": [ "
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Rowx1x2x3x4x5x6x7x8x9x10
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⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
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" ] }, "metadata": {}, "execution_count": 11 } ], "cell_type": "code", "source": [ "y = DataFrame(rand(1:10, 1000, 10), :auto)" ], "metadata": {}, "execution_count": 11 }, { "cell_type": "markdown", "source": [ "and then we can use `first` to peek into its first few rows" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m5×10 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\u001b[1m x4 \u001b[0m\u001b[1m x5 \u001b[0m\u001b[1m x6 \u001b[0m\u001b[1m x7 \u001b[0m\u001b[1m x8 \u001b[0m\u001b[1m x9 \u001b[0m\u001b[1m x10 \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n─────┼──────────────────────────────────────────────────────────────────────\n 1 │ 1 3 6 5 3 3 1 8 8 3\n 2 │ 10 8 1 7 8 3 10 10 7 9\n 3 │ 9 9 9 4 2 8 3 4 10 8\n 4 │ 6 4 6 7 8 2 3 5 8 1\n 5 │ 3 1 8 5 4 6 4 8 5 1", "text/html": [ "
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" ] }, "metadata": {}, "execution_count": 12 } ], "cell_type": "code", "source": [ "first(y, 5)" ], "metadata": {}, "execution_count": 12 }, { "cell_type": "markdown", "source": [ "and `last` to see its bottom rows." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m3×10 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\u001b[1m x4 \u001b[0m\u001b[1m x5 \u001b[0m\u001b[1m x6 \u001b[0m\u001b[1m x7 \u001b[0m\u001b[1m x8 \u001b[0m\u001b[1m x9 \u001b[0m\u001b[1m x10 \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n─────┼──────────────────────────────────────────────────────────────────────\n 1 │ 4 8 1 1 3 3 2 3 8 8\n 2 │ 1 9 4 4 10 6 4 3 8 2\n 3 │ 7 1 4 2 1 8 1 1 7 3", "text/html": [ "
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" ] }, "metadata": {}, "execution_count": 13 } ], "cell_type": "code", "source": [ "last(y, 3)" ], "metadata": {}, "execution_count": 13 }, { "cell_type": "markdown", "source": [ "Using `first` and `last` without number of rows will return a first/last `DataFrameRow` in the `DataFrame`" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1mDataFrameRow\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\u001b[1m x4 \u001b[0m\u001b[1m x5 \u001b[0m\u001b[1m x6 \u001b[0m\u001b[1m x7 \u001b[0m\u001b[1m x8 \u001b[0m\u001b[1m x9 \u001b[0m\u001b[1m x10 \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n─────┼──────────────────────────────────────────────────────────────────────\n 1 │ 1 3 6 5 3 3 1 8 8 3", "text/html": [ "
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" ] }, "metadata": {}, "execution_count": 14 } ], "cell_type": "code", "source": [ "first(y)" ], "metadata": {}, "execution_count": 14 }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1mDataFrameRow\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\u001b[1m x4 \u001b[0m\u001b[1m x5 \u001b[0m\u001b[1m x6 \u001b[0m\u001b[1m x7 \u001b[0m\u001b[1m x8 \u001b[0m\u001b[1m x9 \u001b[0m\u001b[1m x10 \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n──────┼──────────────────────────────────────────────────────────────────────\n 1000 │ 7 1 4 2 1 8 1 1 7 3", "text/html": [ "
DataFrameRow (10 columns)
Rowx1x2x3x4x5x6x7x8x9x10
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" ] }, "metadata": {}, "execution_count": 15 } ], "cell_type": "code", "source": [ "last(y)" ], "metadata": {}, "execution_count": 15 }, { "cell_type": "markdown", "source": [ "## Displaying large data frames\n", "Create a wide and tall data frame:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m100×100 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\u001b[1m x4 \u001b[0m\u001b[1m x5 \u001b[0m\u001b[1m x6 \u001b[0m\u001b[1m x7 \u001b[0m ⋯\n │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float6\u001b[0m ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 0.789962 0.0624244 0.735969 0.460358 0.995564 0.778658 0.6534 ⋯\n 2 │ 0.906916 0.801493 0.916937 0.303229 0.0785325 0.504828 0.1142\n 3 │ 0.191172 0.640318 0.591957 0.768282 0.976985 0.947779 0.0449\n 4 │ 0.152854 0.519964 0.588042 0.586559 0.355327 0.272534 0.7055\n 5 │ 0.438492 0.979425 0.0891575 0.945882 0.730485 0.642267 0.8786 ⋯\n 6 │ 0.978017 0.685007 0.13794 0.527277 0.959323 0.353505 0.8836\n 7 │ 0.355382 0.387504 0.0386789 0.817752 0.920758 0.947085 0.2679\n 8 │ 0.623965 0.573277 0.380789 0.272908 0.484304 0.343541 0.6340\n ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n 94 │ 0.932557 0.702032 0.219581 0.886079 0.588761 0.271663 0.5069 ⋯\n 95 │ 0.957338 0.00108312 0.0452597 0.0415551 0.0603021 0.932539 0.1623\n 96 │ 0.217519 0.0452365 0.96423 0.987456 0.199681 0.927534 0.8782\n 97 │ 0.775986 0.767202 0.244489 0.912083 0.464894 0.428593 0.2336\n 98 │ 0.522384 0.740745 0.807622 0.15677 0.842836 0.485635 0.8578 ⋯\n 99 │ 0.512551 0.294041 0.213607 0.895076 0.816713 0.363689 0.8900\n 100 │ 0.61419 0.176781 0.0414305 0.791225 0.0696568 0.889425 0.0994\n\u001b[36m 94 columns and 85 rows omitted\u001b[0m", "text/html": [ "
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950.9573380.001083120.04525970.04155510.06030210.9325390.1623270.5543320.006260940.4110860.9057090.1471080.5887690.7544560.05491820.6830310.2405430.9177370.5962220.1425230.2085930.05700860.1905650.9669170.459580.516240.4749360.3272850.7647230.7054560.23520.8482330.4265020.002187720.5447340.1766090.3948780.5338750.1292970.1456680.8199160.6132210.07327650.02989990.01450710.4921780.6445730.2894020.02859950.5219270.4710340.8962320.4365830.1816190.9064940.1295310.6096990.7424030.9751990.1617360.7490930.4746840.4765120.9954470.3217330.4647860.2249410.1622950.7810670.9091280.2943590.7326960.3871560.4778050.6177130.1428630.09482520.8386130.4284790.8501490.3601350.5794480.7838990.8043170.7347180.1311840.160230.7161230.8727330.5219060.9662090.4607460.5716130.8739840.1233320.6745250.7523190.9156870.3377970.733894
960.2175190.04523650.964230.9874560.1996810.9275340.8782670.3782070.8720080.2380980.2576140.6339710.9767850.6632630.3906870.3532850.6138820.7418350.1079310.1596430.9569330.5049970.28030.4331690.01562740.8790040.7261080.6834160.9258060.5938910.4546650.1323480.3662130.02345350.7299590.5570510.2212220.5683130.03487860.3125950.9897440.08050060.8247320.04774570.933470.4281670.3264580.4323810.1409560.5818440.1839740.8160840.3902740.8133280.7016940.8380390.3454570.9477280.3299880.871440.7179560.4039990.2004290.2069370.5905910.6318040.3418310.7200180.228410.5553940.2362540.5993660.1663110.6356770.9354860.3648330.9246160.9489360.4789770.9515910.06337990.108120.9546490.2201050.587440.9788210.2129940.8167670.8522910.441560.6397630.03612410.9805160.1398430.9990620.6374380.2182610.3031850.8749530.308514
970.7759860.7672020.2444890.9120830.4648940.4285930.2336540.2704410.6627840.6854290.9130070.4198680.04476730.1006360.5301660.7596010.1056960.8782490.8123190.1334920.1237470.6278620.8031890.1761590.8936050.5098330.895630.8381780.8247330.219170.2702750.6319830.4805380.997440.006247130.08208330.4436150.1645360.3090130.261520.03155890.2660620.2386580.7401170.4207590.8942840.4011520.4067650.2189360.5094360.4764130.6422630.8140020.4960540.9236060.2203040.7717780.2952050.9943860.7605250.1796460.2862790.009846920.1066950.9236220.9197960.7198810.9000470.3878180.7940210.4869810.921230.08853110.6673910.618220.0954120.7844220.3955690.1078450.2810750.2372780.4097290.6694980.05390780.5206310.2010340.4765080.9570750.9202350.3765510.3608920.7938290.5722120.6931870.6546350.9166160.5875880.5601660.2004090.0910738
980.5223840.7407450.8076220.156770.8428360.4856350.8578480.9222810.4094670.09328160.1783180.7321830.02230560.6434920.9545210.9161750.6187340.9615020.2144510.5935540.2843020.6522270.6629530.9028470.7406480.4188430.5983260.3201450.6269250.3324440.944810.009519620.4082720.625490.7273590.7138450.5287160.04170950.9531020.4421720.9822320.3569230.6402110.9388850.2790690.9977780.05676640.5736130.2396740.06708270.5426540.3739620.7502050.2371240.6290470.3740390.7110360.6682410.6857650.3392480.3674680.4610480.483420.32690.7176110.9475210.3186310.01130160.2002630.5660340.1328540.221670.4216060.8517070.4113020.4837420.5836980.7555730.9838590.4137060.9696350.7920340.5685180.2269610.02103440.703620.09064330.9109670.5606270.1507920.146770.660630.0510290.7458240.6132130.268470.07782110.03435510.07339160.109837
990.5125510.2940410.2136070.8950760.8167130.3636890.8900630.3592140.7721960.6418270.6092220.7092870.4479940.4892380.3207490.9197990.3246570.6608150.8378990.06835060.1546250.07735420.1844970.333720.9077790.523560.2438730.08814710.2391570.1933520.7250130.3571060.7379410.4908110.7748280.4477740.4680670.24180.9393110.2143870.1705220.1161660.0772450.3312240.8084390.3340340.367610.967920.8931160.6603930.7449930.9363950.792730.4092490.2921890.1387850.258670.169990.3917770.9067320.6065010.4757050.5575340.01248580.7141360.1569390.877240.5400450.1487720.108240.3068880.4092290.7569490.648360.2094190.6774180.5543420.09371780.609640.1278780.2214880.516330.1035580.4875930.9982540.4954580.09623330.04971430.188010.2357060.2541620.3279160.05505990.8795540.4222940.5097370.1877780.2700360.8777070.467502
1000.614190.1767810.04143050.7912250.06965680.8894250.09942570.8090570.536310.2964340.8936240.651670.8483230.8797340.8599810.3162210.4404160.3846870.1220390.6447870.2627580.1517280.2258960.7243590.1978220.7192090.06737430.2984270.9677210.4434250.3323890.09937150.500760.1524710.02160560.4541340.4533290.7528430.2230690.7175380.8612530.03905360.385160.1158520.21240.8191440.4535410.270310.3840870.1888180.0627450.5648580.359680.6725910.8313380.03516650.04296040.01223830.6996130.8454580.9314690.8931510.07069750.867390.2755420.03341330.1741980.1080960.6928650.7237920.7328220.1887440.4427340.3636430.6090890.2134750.6292680.9394750.7733750.7556430.64450.8251810.2744810.05177580.4334480.8745570.4177650.8671310.5946010.8142860.7976310.7363570.71230.9389630.6695590.5747950.4151250.413070.9341990.179651
" ] }, "metadata": {}, "execution_count": 16 } ], "cell_type": "code", "source": [ "df = DataFrame(rand(100, 100), :auto)" ], "metadata": {}, "execution_count": 16 }, { "cell_type": "markdown", "source": [ "we can see that 92 of its columns were not printed. Also we get its first 30 rows. You can easily change this behavior by changing the value of `ENV[\"LINES\"]` and `ENV[\"COLUMNS\"]`." ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100×100 DataFrame\n", " Row │ x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 ⋯\n", " │ Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Flo ⋯\n", "─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", " 1 │ 0.789962 0.0624244 0.735969 0.460358 0.995564 0.778658 0.653409 0.8649 0.744414 0.718615 0.817303 0.842155 0.90281 0.678668 0.196716 0.400046 0.975851 0.408243 0.1 ⋯\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 82 columns and 99 rows omitted" ] } ], "cell_type": "code", "source": [ "withenv(\"LINES\" => 10, \"COLUMNS\" => 200) do\n", " show(df)\n", "end" ], "metadata": {}, "execution_count": 17 }, { "cell_type": "markdown", "source": [ "### Most elementary get and set operations\n", "Given the `DataFrame` `x` we have created earlier, here are various ways to grab one of its columns as a `Vector`." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\u001b[1m C \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m String \u001b[0m\n─────┼──────────────────────────\n 1 │ 1 1.0 a\n 2 │ 2 \u001b[90m missing \u001b[0m b", "text/html": [ "
2×3 DataFrame
RowABC
Int64Float64?String
111.0a
22missingb
" ] }, "metadata": {}, "execution_count": 18 } ], "cell_type": "code", "source": [ "x = DataFrame(A=[1, 2], B=[1.0, missing], C=[\"a\", \"b\"])" ], "metadata": {}, "execution_count": 18 }, { "cell_type": "markdown", "source": [ "all get the vector stored in our DataFrame without copying it" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "([1, 2], [1, 2], [1, 2])" }, "metadata": {}, "execution_count": 19 } ], "cell_type": "code", "source": [ "x.A, x[!, 1], x[!, :A]" ], "metadata": {}, "execution_count": 19 }, { "cell_type": "markdown", "source": [ "the same using string indexing" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "([1, 2], [1, 2])" }, "metadata": {}, "execution_count": 20 } ], "cell_type": "code", "source": [ "x.\"A\", x[!, \"A\"]" ], "metadata": {}, "execution_count": 20 }, { "cell_type": "markdown", "source": [ "note that this creates a copy" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "2-element Vector{Int64}:\n 1\n 2" }, "metadata": {}, "execution_count": 21 } ], "cell_type": "code", "source": [ "x[:, 1]" ], "metadata": {}, "execution_count": 21 }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "false" }, "metadata": {}, "execution_count": 22 } ], "cell_type": "code", "source": [ "x[:, 1] === x[:, 1]" ], "metadata": {}, "execution_count": 22 }, { "cell_type": "markdown", "source": [ "To grab one row as a `DataFrame`, we can index as follows." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m1×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\u001b[1m C \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m String \u001b[0m\n─────┼─────────────────────────\n 1 │ 1 1.0 a", "text/html": [ "
1×3 DataFrame
RowABC
Int64Float64?String
111.0a
" ] }, "metadata": {}, "execution_count": 23 } ], "cell_type": "code", "source": [ "x[1:1, :]" ], "metadata": {}, "execution_count": 23 }, { "cell_type": "markdown", "source": [ "this produces a DataFrameRow which is treated as 1-dimensional object similar to a NamedTuple" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1mDataFrameRow\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\u001b[1m C \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m String \u001b[0m\n─────┼─────────────────────────\n 1 │ 1 1.0 a", "text/html": [ "
DataFrameRow (3 columns)
RowABC
Int64Float64?String
111.0a
" ] }, "metadata": {}, "execution_count": 24 } ], "cell_type": "code", "source": [ "x[1, :]" ], "metadata": {}, "execution_count": 24 }, { "cell_type": "markdown", "source": [ "We can grab a single cell or element with the same syntax to grab an element of an array." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "1" }, "metadata": {}, "execution_count": 25 } ], "cell_type": "code", "source": [ "x[1, 1]" ], "metadata": {}, "execution_count": 25 }, { "cell_type": "markdown", "source": [ "or a new `DataFrame` that is a subset of rows and columns" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×2 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Float64? \u001b[0m\n─────┼──────────────────\n 1 │ 1 1.0\n 2 │ 2 \u001b[90m missing \u001b[0m", "text/html": [ "
2×2 DataFrame
RowAB
Int64Float64?
111.0
22missing
" ] }, "metadata": {}, "execution_count": 26 } ], "cell_type": "code", "source": [ "x[1:2, 1:2]" ], "metadata": {}, "execution_count": 26 }, { "cell_type": "markdown", "source": [ "You can also use `Regex` to select columns and `Not` from InvertedIndices.jl both to select rows and columns" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m1×1 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\n │\u001b[90m Int64 \u001b[0m\n─────┼───────\n 1 │ 2", "text/html": [ "
1×1 DataFrame
RowA
Int64
12
" ] }, "metadata": {}, "execution_count": 27 } ], "cell_type": "code", "source": [ "x[Not(1), r\"A\"]" ], "metadata": {}, "execution_count": 27 }, { "cell_type": "markdown", "source": [ "`!` indicates that underlying columns are not copied" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×2 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m B \u001b[0m\u001b[1m C \u001b[0m\n │\u001b[90m Float64? \u001b[0m\u001b[90m String \u001b[0m\n─────┼───────────────────\n 1 │ 1.0 a\n 2 │\u001b[90m missing \u001b[0m b", "text/html": [ "
2×2 DataFrame
RowBC
Float64?String
11.0a
2missingb
" ] }, "metadata": {}, "execution_count": 28 } ], "cell_type": "code", "source": [ "x[!, Not(1)]" ], "metadata": {}, "execution_count": 28 }, { "cell_type": "markdown", "source": [ "`:` means that the columns will get copied" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×2 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m B \u001b[0m\u001b[1m C \u001b[0m\n │\u001b[90m Float64? \u001b[0m\u001b[90m String \u001b[0m\n─────┼───────────────────\n 1 │ 1.0 a\n 2 │\u001b[90m missing \u001b[0m b", "text/html": [ "
2×2 DataFrame
RowBC
Float64?String
11.0a
2missingb
" ] }, "metadata": {}, "execution_count": 29 } ], "cell_type": "code", "source": [ "x[:, Not(1)]" ], "metadata": {}, "execution_count": 29 }, { "cell_type": "markdown", "source": [ "Assignment of a scalar to a data frame can be done in ranges using broadcasting:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\u001b[1m C \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m String \u001b[0m\n─────┼─────────────────────────\n 1 │ 1 1.0 a\n 2 │ 1 1.0 b", "text/html": [ "
2×3 DataFrame
RowABC
Int64Float64?String
111.0a
211.0b
" ] }, "metadata": {}, "execution_count": 30 } ], "cell_type": "code", "source": [ "x[1:2, 1:2] .= 1\n", "x" ], "metadata": {}, "execution_count": 30 }, { "cell_type": "markdown", "source": [ "Assignment of a vector of length equal to the number of assigned rows using broadcasting" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\u001b[1m C \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m String \u001b[0m\n─────┼─────────────────────────\n 1 │ 1 1.0 a\n 2 │ 2 2.0 b", "text/html": [ "
2×3 DataFrame
RowABC
Int64Float64?String
111.0a
222.0b
" ] }, "metadata": {}, "execution_count": 31 } ], "cell_type": "code", "source": [ "x[1:2, 1:2] .= [1, 2]\n", "x" ], "metadata": {}, "execution_count": 31 }, { "cell_type": "markdown", "source": [ "Assignment or of another data frame of matching size and column names, again using broadcasting:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\u001b[1m C \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m String \u001b[0m\n─────┼─────────────────────────\n 1 │ 5 6.0 a\n 2 │ 7 8.0 b", "text/html": [ "
2×3 DataFrame
RowABC
Int64Float64?String
156.0a
278.0b
" ] }, "metadata": {}, "execution_count": 32 } ], "cell_type": "code", "source": [ "x[1:2, 1:2] .= DataFrame([5 6; 7 8], [:A, :B])\n", "x" ], "metadata": {}, "execution_count": 32 }, { "cell_type": "markdown", "source": [ "**Caution**\n", "\n", "With `df[!, :col]` and `df.col` syntax you get a direct (non copying) access to a column of a data frame.\n", "This is potentially unsafe as you can easily corrupt data in the `df` data frame if you resize, sort, etc. the column obtained in this way.\n", "Therefore such access should be used with caution.\n", "\n", "Similarly `df[!, cols]` when `cols` is a collection of columns produces a new data frame that holds the same (not copied) columns as the source `df` data frame. Similarly, modifying the data frame obtained via `df[!, cols]` might cause problems with the consistency of `df`.\n", "\n", "The `df[:, :col]` and `df[:, cols]` syntaxes always copy columns so they are safe to use (and should generally be preferred except for performance or memory critical use cases)." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Here are examples of how `Cols` and `Between` can be used to select columns of a data frame." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m4×5 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\u001b[1m x4 \u001b[0m\u001b[1m x5 \u001b[0m\n │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼───────────────────────────────────────────────────\n 1 │ 0.143664 0.243967 0.541132 0.90432 0.253281\n 2 │ 0.649823 0.974319 0.523402 0.547555 0.906718\n 3 │ 0.532381 0.154004 0.618354 0.0428888 0.897352\n 4 │ 0.878731 0.371432 0.413427 0.829586 0.771033", "text/html": [ "
4×5 DataFrame
Rowx1x2x3x4x5
Float64Float64Float64Float64Float64
10.1436640.2439670.5411320.904320.253281
20.6498230.9743190.5234020.5475550.906718
30.5323810.1540040.6183540.04288880.897352
40.8787310.3714320.4134270.8295860.771033
" ] }, "metadata": {}, "execution_count": 33 } ], "cell_type": "code", "source": [ "x = DataFrame(rand(4, 5), :auto)" ], "metadata": {}, "execution_count": 33 }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m4×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\u001b[1m x4 \u001b[0m\n │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼───────────────────────────────\n 1 │ 0.243967 0.541132 0.90432\n 2 │ 0.974319 0.523402 0.547555\n 3 │ 0.154004 0.618354 0.0428888\n 4 │ 0.371432 0.413427 0.829586", "text/html": [ "
4×3 DataFrame
Rowx2x3x4
Float64Float64Float64
10.2439670.5411320.90432
20.9743190.5234020.547555
30.1540040.6183540.0428888
40.3714320.4134270.829586
" ] }, "metadata": {}, "execution_count": 34 } ], "cell_type": "code", "source": [ "x[:, Between(:x2, :x4)]" ], "metadata": {}, "execution_count": 34 }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m4×4 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\u001b[1m x4 \u001b[0m\n │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼─────────────────────────────────────────\n 1 │ 0.143664 0.243967 0.541132 0.90432\n 2 │ 0.649823 0.974319 0.523402 0.547555\n 3 │ 0.532381 0.154004 0.618354 0.0428888\n 4 │ 0.878731 0.371432 0.413427 0.829586", "text/html": [ "
4×4 DataFrame
Rowx1x2x3x4
Float64Float64Float64Float64
10.1436640.2439670.5411320.90432
20.6498230.9743190.5234020.547555
30.5323810.1540040.6183540.0428888
40.8787310.3714320.4134270.829586
" ] }, "metadata": {}, "execution_count": 35 } ], "cell_type": "code", "source": [ "x[:, Cols(\"x1\", Between(\"x2\", \"x4\"))]" ], "metadata": {}, "execution_count": 35 }, { "cell_type": "markdown", "source": [ "## Views\n", "You can simply create a view of a `DataFrame` (it is more efficient than creating a materialized selection). Here are the possible return value options." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "2-element view(::Vector{Float64}, 1:2) with eltype Float64:\n 0.14366370663453387\n 0.6498227286696711" }, "metadata": {}, "execution_count": 36 } ], "cell_type": "code", "source": [ "@view x[1:2, 1]" ], "metadata": {}, "execution_count": 36 }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "0-dimensional view(::Vector{Float64}, 1) with eltype Float64:\n0.14366370663453387" }, "metadata": {}, "execution_count": 37 } ], "cell_type": "code", "source": [ "@view x[1, 1]" ], "metadata": {}, "execution_count": 37 }, { "cell_type": "markdown", "source": [ "a DataFrameRow, the same as for x[1, 1:2] without a view" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1mDataFrameRow\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\n │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼────────────────────\n 1 │ 0.143664 0.243967", "text/html": [ "
DataFrameRow (2 columns)
Rowx1x2
Float64Float64
10.1436640.243967
" ] }, "metadata": {}, "execution_count": 38 } ], "cell_type": "code", "source": [ "@view x[1, 1:2]" ], "metadata": {}, "execution_count": 38 }, { "cell_type": "markdown", "source": [ "a SubDataFrame" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×2 SubDataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\n │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼────────────────────\n 1 │ 0.143664 0.243967\n 2 │ 0.649823 0.974319", "text/html": [ "
2×2 SubDataFrame
Rowx1x2
Float64Float64
10.1436640.243967
20.6498230.974319
" ] }, "metadata": {}, "execution_count": 39 } ], "cell_type": "code", "source": [ "@view x[1:2, 1:2]" ], "metadata": {}, "execution_count": 39 }, { "cell_type": "markdown", "source": [ "## Adding new columns to a data frame" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m0×0 DataFrame\u001b[0m", "text/html": [ "
0×0 DataFrame
" ] }, "metadata": {}, "execution_count": 40 } ], "cell_type": "code", "source": [ "df = DataFrame()" ], "metadata": {}, "execution_count": 40 }, { "cell_type": "markdown", "source": [ "using `setproperty!` (element assignment)" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m3×1 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m a \u001b[0m\n │\u001b[90m Int64 \u001b[0m\n─────┼───────\n 1 │ 1\n 2 │ 2\n 3 │ 3", "text/html": [ "
3×1 DataFrame
Rowa
Int64
11
22
33
" ] }, "metadata": {}, "execution_count": 41 } ], "cell_type": "code", "source": [ "x = [1, 2, 3]\n", "df.a = x\n", "df" ], "metadata": {}, "execution_count": 41 }, { "cell_type": "markdown", "source": [ "no copy is performed (sharing the same memory address)" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "true" }, "metadata": {}, "execution_count": 42 } ], "cell_type": "code", "source": [ "df.a === x" ], "metadata": {}, "execution_count": 42 }, { "cell_type": "markdown", "source": [ "using `setindex!`" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m3×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m a \u001b[0m\u001b[1m b \u001b[0m\u001b[1m c \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n─────┼─────────────────────\n 1 │ 1 1 1\n 2 │ 2 2 2\n 3 │ 3 3 3", "text/html": [ "
3×3 DataFrame
Rowabc
Int64Int64Int64
1111
2222
3333
" ] }, "metadata": {}, "execution_count": 43 } ], "cell_type": "code", "source": [ "df[!, :b] = x\n", "df[:, :c] = x\n", "df" ], "metadata": {}, "execution_count": 43 }, { "cell_type": "markdown", "source": [ "no copy is performed" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "true" }, "metadata": {}, "execution_count": 44 } ], "cell_type": "code", "source": [ "df.b === x" ], "metadata": {}, "execution_count": 44 }, { "cell_type": "markdown", "source": [ "With copying\n", "`!` and `:` has different effects" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "false" }, "metadata": {}, "execution_count": 45 } ], "cell_type": "code", "source": [ "df.c === x" ], "metadata": {}, "execution_count": 45 }, { "cell_type": "markdown", "source": [ "Element-wise assignment" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m3×5 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m a \u001b[0m\u001b[1m b \u001b[0m\u001b[1m c \u001b[0m\u001b[1m d \u001b[0m\u001b[1m e \u001b[0m\n │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n─────┼───────────────────────────────────\n 1 │ 1 1 1 1 1\n 2 │ 2 2 2 2 2\n 3 │ 3 3 3 3 3", "text/html": [ "
3×5 DataFrame
Rowabcde
Int64Int64Int64Int64Int64
111111
222222
333333
" ] }, "metadata": {}, "execution_count": 46 } ], "cell_type": "code", "source": [ "df[!, :d] .= x\n", "df[:, :e] .= x\n", "df" ], "metadata": {}, "execution_count": 46 }, { "cell_type": "markdown", "source": [ "both copy, so in this case `!` and `:` has the same effect" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "(false, false)" }, "metadata": {}, "execution_count": 47 } ], "cell_type": "code", "source": [ "df.d === x, df.e === x" ], "metadata": {}, "execution_count": 47 }, { "cell_type": "markdown", "source": [ "note that in our data frame columns `:a` and `:b` store the vector `x` (not a copy)" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "true" }, "metadata": {}, "execution_count": 48 } ], "cell_type": "code", "source": [ "df.a === df.b === x" ], "metadata": {}, "execution_count": 48 }, { "cell_type": "markdown", "source": [ "This can lead to silent errors. For example this code leads to a bug (note that calling `pairs` on `eachcol(df)` creates an iterator of (column name, column) pairs):" ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a: 3\n", "b: 2\n", "c: 3\n", "d: 3\n", "e: 3\n" ] } ], "cell_type": "code", "source": [ "try\n", " for (n, c) in pairs(eachcol(df))\n", " println(\"$n: \", pop!(c))\n", " end\n", "catch e\n", " show(e)\n", "end" ], "metadata": {}, "execution_count": 49 }, { "cell_type": "markdown", "source": [ "note that for column `:b` we printed `2` as `3` was removed from it when we used `pop!` on column `:a`.\n", "Such mistakes sometimes happen. Because of this DataFrames.jl performs consistency checks before doing an expensive operation (most notably before showing a data frame)." ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AssertionError(\"Data frame is corrupt: length of column :c (2) does not match length of column 1 (1). The column vector has likely been resized unintentionally (either directly or because it is shared with another data frame).\")" ] } ], "cell_type": "code", "source": [ "try\n", " show(df)\n", "catch e\n", " show(e)\n", "end" ], "metadata": {}, "execution_count": 50 }, { "cell_type": "markdown", "source": [ "We can investigate the columns to find out what happened:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "5-element Vector{Pair{Symbol, AbstractVector}}:\n :a => [1]\n :b => [1]\n :c => [1, 2]\n :d => [1, 2]\n :e => [1, 2]" }, "metadata": {}, "execution_count": 51 } ], "cell_type": "code", "source": [ "collect(pairs(eachcol(df)))" ], "metadata": {}, "execution_count": 51 }, { "cell_type": "markdown", "source": [ "The output confirms that the data frame `df` got corrupted.\n", "DataFrames.jl supports a complete set of `getindex`, `getproperty`, `setindex!`, `setproperty!`, `view`, broadcasting, and broadcasting assignment operations. The details are explained here: http://juliadata.github.io/DataFrames.jl/latest/lib/indexing/." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Comparisons" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "using DataFrames" ], "metadata": {}, "execution_count": 52 }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\n │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼──────────────────────────────\n 1 │ 0.938257 0.811633 0.362354\n 2 │ 0.975227 0.753141 0.141313", "text/html": [ "
2×3 DataFrame
Rowx1x2x3
Float64Float64Float64
10.9382570.8116330.362354
20.9752270.7531410.141313
" ] }, "metadata": {}, "execution_count": 53 } ], "cell_type": "code", "source": [ "df = DataFrame(rand(2, 3), :auto)" ], "metadata": {}, "execution_count": 53 }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\n │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼──────────────────────────────\n 1 │ 0.938257 0.811633 0.362354\n 2 │ 0.975227 0.753141 0.141313", "text/html": [ "
2×3 DataFrame
Rowx1x2x3
Float64Float64Float64
10.9382570.8116330.362354
20.9752270.7531410.141313
" ] }, "metadata": {}, "execution_count": 54 } ], "cell_type": "code", "source": [ "df2 = copy(df)" ], "metadata": {}, "execution_count": 54 }, { "cell_type": "markdown", "source": [ "compares column names and contents" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "true" }, "metadata": {}, "execution_count": 55 } ], "cell_type": "code", "source": [ "df == df2" ], "metadata": {}, "execution_count": 55 }, { "cell_type": "markdown", "source": [ "create a minimally different data frame and use `isapprox` for comparison" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m2×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\n │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼──────────────────────────────\n 1 │ 0.938257 0.811633 0.362354\n 2 │ 0.975227 0.753141 0.141313", "text/html": [ "
2×3 DataFrame
Rowx1x2x3
Float64Float64Float64
10.9382570.8116330.362354
20.9752270.7531410.141313
" ] }, "metadata": {}, "execution_count": 56 } ], "cell_type": "code", "source": [ "df3 = df2 .+ eps()" ], "metadata": {}, "execution_count": 56 }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "false" }, "metadata": {}, "execution_count": 57 } ], "cell_type": "code", "source": [ "df == df3" ], "metadata": {}, "execution_count": 57 }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "true" }, "metadata": {}, "execution_count": 58 } ], "cell_type": "code", "source": [ "isapprox(df, df3)" ], "metadata": {}, "execution_count": 58 }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "false" }, "metadata": {}, "execution_count": 59 } ], "cell_type": "code", "source": [ "isapprox(df, df3, atol=eps() / 2)" ], "metadata": {}, "execution_count": 59 }, { "cell_type": "markdown", "source": [ "`missings` are handled as in Julia Base" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "\u001b[1m1×1 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m a \u001b[0m\n │\u001b[90m Missing \u001b[0m\n─────┼─────────\n 1 │\u001b[90m missing \u001b[0m", "text/html": [ "
1×1 DataFrame
Rowa
Missing
1missing
" ] }, "metadata": {}, "execution_count": 60 } ], "cell_type": "code", "source": [ "df = DataFrame(a=missing)" ], "metadata": {}, "execution_count": 60 }, { "cell_type": "markdown", "source": [ "Equality test shows missing." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "missing" }, "metadata": {}, "execution_count": 61 } ], "cell_type": "code", "source": [ "df == df" ], "metadata": {}, "execution_count": 61 }, { "cell_type": "markdown", "source": [ "The same object?" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "true" }, "metadata": {}, "execution_count": 62 } ], "cell_type": "code", "source": [ "df === df" ], "metadata": {}, "execution_count": 62 }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "true" }, "metadata": {}, "execution_count": 63 } ], "cell_type": "code", "source": [ "isequal(df, df)" ], "metadata": {}, "execution_count": 63 }, { "cell_type": "markdown", "source": [ "---\n", "\n", "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" ], "metadata": {} } ], "nbformat_minor": 3, "metadata": { "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.10.5" }, "kernelspec": { "name": "julia-1.10", "display_name": "Julia 1.10.5", "language": "julia" } }, "nbformat": 4 }